{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "### Sequential Indicator Simulation \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "Here's a simple workflow for spatial simulation with sequential indicator simulation. This step is critical for:\n",
    "\n",
    "1. Prediction away from spatial data with uncertainty.\n",
    "2. Spatial cross validation.\n",
    "3. Spatial uncertainty modeling with realistic spatial feature realizations.\n",
    "\n",
    "We use indicator simulation for the case of categorical features and when control of spatial continuity over feature magnitude is essential for a continuous feature.\n",
    "\n",
    "First let's explain the concept of spatial simulation.\n",
    "\n",
    "#### Spatial Simulation\n",
    "\n",
    "\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data_MV_biased.csv available at https://git.io/fhgu0.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting\n",
    "import geostatspy.GSLIB as GSLIB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Load the Required Libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting\n",
    "import geostatspy.GSLIB as GSLIB\n",
    "import geostatspy.geostats as geostats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0.25'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geostatspy\n",
    "geostatspy.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                                     # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. We will also extra a limited sample so that the spatial samples are not too dense.  This way we can observe more of the heterogeneity from the simulation with the spatial continuity model, rather than mostly data driven heterogeneity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>400.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.136711</td>\n",
       "      <td>148.350740</td>\n",
       "      <td>4812.503090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>282</td>\n",
       "      <td>449</td>\n",
       "      <td>300.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148719</td>\n",
       "      <td>86.109630</td>\n",
       "      <td>4454.324696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>254</td>\n",
       "      <td>410</td>\n",
       "      <td>300.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.168196</td>\n",
       "      <td>602.656287</td>\n",
       "      <td>3766.141917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>336</td>\n",
       "      <td>530</td>\n",
       "      <td>560.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094792</td>\n",
       "      <td>1.517269</td>\n",
       "      <td>5918.572431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "      <td>500.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.087282</td>\n",
       "      <td>4.574283</td>\n",
       "      <td>4990.461286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>290</td>\n",
       "      <td>457</td>\n",
       "      <td>720.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.134839</td>\n",
       "      <td>11.037612</td>\n",
       "      <td>3716.261543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>400.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.158800</td>\n",
       "      <td>174.258300</td>\n",
       "      <td>4101.371674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>231</td>\n",
       "      <td>380</td>\n",
       "      <td>750.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.138428</td>\n",
       "      <td>10.960150</td>\n",
       "      <td>4179.653364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>400.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.174147</td>\n",
       "      <td>860.780327</td>\n",
       "      <td>4715.552209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>210</td>\n",
       "      <td>344</td>\n",
       "      <td>190.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.179469</td>\n",
       "      <td>371.556404</td>\n",
       "      <td>4614.047656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>284</td>\n",
       "      <td>451</td>\n",
       "      <td>320.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.149400</td>\n",
       "      <td>131.864248</td>\n",
       "      <td>4332.562518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>57</td>\n",
       "      <td>89</td>\n",
       "      <td>890.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.132899</td>\n",
       "      <td>2.815137</td>\n",
       "      <td>4782.783536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>362</td>\n",
       "      <td>574</td>\n",
       "      <td>320.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.180336</td>\n",
       "      <td>957.426748</td>\n",
       "      <td>4427.866875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>141</td>\n",
       "      <td>227</td>\n",
       "      <td>240.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094064</td>\n",
       "      <td>2.479957</td>\n",
       "      <td>6144.640275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>177</td>\n",
       "      <td>288</td>\n",
       "      <td>570.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089293</td>\n",
       "      <td>2.411069</td>\n",
       "      <td>4453.078357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>350</td>\n",
       "      <td>554</td>\n",
       "      <td>730.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.120533</td>\n",
       "      <td>5.301364</td>\n",
       "      <td>4493.640123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>58</td>\n",
       "      <td>90</td>\n",
       "      <td>900.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.126350</td>\n",
       "      <td>1.838804</td>\n",
       "      <td>4288.503765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>356</td>\n",
       "      <td>561</td>\n",
       "      <td>520.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.109792</td>\n",
       "      <td>11.131290</td>\n",
       "      <td>5642.804784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>239</td>\n",
       "      <td>390</td>\n",
       "      <td>500.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.112954</td>\n",
       "      <td>12.726682</td>\n",
       "      <td>5496.000633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>117</td>\n",
       "      <td>190</td>\n",
       "      <td>250.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.176423</td>\n",
       "      <td>476.150652</td>\n",
       "      <td>4488.425336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  Unnamed: 0      X      Y  Facies  Porosity        Perm           AI\n",
       "0      20          29  400.0  700.0     1.0  0.136711  148.350740  4812.503090\n",
       "1     282         449  300.0  539.0     1.0  0.148719   86.109630  4454.324696\n",
       "2     254         410  300.0  679.0     1.0  0.168196  602.656287  3766.141917\n",
       "3     336         530  560.0  189.0     0.0  0.094792    1.517269  5918.572431\n",
       "4      28          39  500.0  600.0     0.0  0.087282    4.574283  4990.461286\n",
       "5     290         457  720.0  129.0     1.0  0.134839   11.037612  3716.261543\n",
       "6      18          27  400.0  900.0     1.0  0.158800  174.258300  4101.371674\n",
       "7     231         380  750.0  189.0     1.0  0.138428   10.960150  4179.653364\n",
       "8      21          30  400.0  600.0     1.0  0.174147  860.780327  4715.552209\n",
       "9     210         344  190.0  729.0     1.0  0.179469  371.556404  4614.047656\n",
       "10    284         451  320.0  559.0     1.0  0.149400  131.864248  4332.562518\n",
       "11     57          89  890.0  894.0     1.0  0.132899    2.815137  4782.783536\n",
       "12    362         574  320.0  829.0     1.0  0.180336  957.426748  4427.866875\n",
       "13    141         227  240.0   79.0     0.0  0.094064    2.479957  6144.640275\n",
       "14    177         288  570.0  579.0     0.0  0.089293    2.411069  4453.078357\n",
       "15    350         554  730.0  179.0     1.0  0.120533    5.301364  4493.640123\n",
       "16     58          90  900.0  894.0     0.0  0.126350    1.838804  4288.503765\n",
       "17    356         561  520.0  729.0     1.0  0.109792   11.131290  5642.804784\n",
       "18    239         390  500.0  659.0     0.0  0.112954   12.726682  5496.000633\n",
       "19    117         190  250.0  729.0     1.0  0.176423  476.150652  4488.425336"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"sample_data_MV_biased.csv\")             # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\")\n",
    "df.describe()                                             # summary statistics \n",
    "df = df.sample(50)                                        # extract 50 samples\n",
    "df = df.reset_index()                                     # reset the record index \n",
    "df.head(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sequential Indicator Simulation\n",
    "\n",
    "Let's jump right to building a variety of models with simulation and visualizing the results.  We will start with multiple realizations.  We will assume a variogram and use simple kriging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73073\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n",
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73074\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n",
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73075\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sequential Indicator Simulation with Simple Kriging Multiple Realizations \n",
    "nx = 100; ny = 100; xsiz = 10.0; ysiz = 10.0; xmn = 5.0; ymn = 5.0; nxdis = 1; nydis = 1\n",
    "ndmin = 0; ndmax = 10; nodmax = 10; radius = 400; skmean = 0\n",
    "tmin = -999; tmax = 999\n",
    "dummy_trend = np.zeros((10,10))            # the current version requires trend input - if wrong size it is ignored \n",
    "\n",
    "ncut = 2                                   # number of facies\n",
    "thresh = [0,1]                             # the facies categories (use consisten order)\n",
    "gcdf = [0.4,0.6]                           # the global proportions of the categories\n",
    "varios = []                                # the variogram list\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=400,hmin1=100)) # shale indicator variogram\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=400,hmin1=100)) # sand indicator variogram\n",
    "\n",
    "sim_ik1 = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73073,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=0,vario=varios)\n",
    "\n",
    "sim_ik2 = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73074,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=0,vario=varios)\n",
    "\n",
    "sim_ik3 = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73075,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=0,vario=varios)\n",
    "\n",
    "\n",
    "xmin = 0.0; xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cmap = plt.cm.inferno # plotting parameters\n",
    "\n",
    "plt.subplot(131)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik1,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Realization 1','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplot(132)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik2,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Realization 2','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplot(133)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik3,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Realization 3','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=4.0, top=1.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe the spatial continuity, relative proportions of facies and the conditioning to the available data. Let's confirm that the representative proportions from the data are honored (the global cdf of 40% shale and 60% sand)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(141)\n",
    "plt.hist(df['Facies'].values,bins=2,density=True,cumulative = True,alpha=0.8,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Well Data Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(142)\n",
    "plt.hist(sim_ik1.flatten(),bins=2,density=True,cumulative = True,alpha=0.8,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 1 Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(143)\n",
    "plt.hist(sim_ik2.flatten(),bins=2,density=True,cumulative = True,alpha=0.8,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 2 Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(144)\n",
    "plt.hist(sim_ik3.flatten(),bins=2,density=True,cumulative = True,alpha=0.8,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 3 Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1., wspace=0.2, hspace=0.2)\n",
    "plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good, we have some ergodic fluctuations, but that is expected. \n",
    "\n",
    "#### Changing the Global Stationary Cateogrical Proportions\n",
    "\n",
    "Let's run three realizations and significantly change the global stationary proportions and then check the results.\n",
    "\n",
    "* the global proportion is like the mean in simple kriging\n",
    "\n",
    "* as we move away from data, there is more weight on the global proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73073\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n",
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73074\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n",
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73075\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sequential Indicator Simulation with Simple Kriging Multiple Realizations \n",
    "nx = 100; ny = 100; xsiz = 10.0; ysiz = 10.0; xmn = 5.0; ymn = 5.0; nxdis = 1; nydis = 1\n",
    "ndmin = 0; ndmax = 10; nodmax = 10; radius = 400; skmean = 0\n",
    "tmin = -999; tmax = 999\n",
    "dummy_trend = np.zeros((10,10))            # the current version requires trend input - if wrong size it is ignored \n",
    "\n",
    "ncut = 2                                   # number of facies\n",
    "thresh = [0,1]                             # the facies categories (use consisten order)\n",
    "varios = []                                # the variogram list\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=100,hmin1=100)) # shale indicator variogram\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=100,hmin1=100)) # sand indicator variogram\n",
    "\n",
    "gcdf1 = [0.3,0.7]                           # the global proportions of the categories\n",
    "sim_ik_s1 = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf1,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73073,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=0,vario=varios)\n",
    "\n",
    "gcdf2 = [0.4,0.6]                           # the global proportions of the categories\n",
    "sim_ik_s2 = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf2,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73074,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=0,vario=varios)\n",
    "\n",
    "gcdf3 = [0.5,0.5]                           # the global proportions of the categories\n",
    "sim_ik_s3 = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf3,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73075,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=0,vario=varios)\n",
    "\n",
    "\n",
    "xmin = 0.0; xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cmap = plt.cm.inferno # plotting parameters\n",
    "\n",
    "plt.subplot(131)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik_s1,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Realization 1','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplot(132)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik_s2,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Realization 2','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplot(133)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik_s3,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Realization 3','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=4.0, top=1.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the proportions again and see how close we got to the global porportion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(141)\n",
    "plt.hist(df['Facies'].values,bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Well Data Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(142)\n",
    "plt.hist(sim_ik_s1.flatten(),bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 1, Scenario 70% Sand Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(143)\n",
    "plt.hist(sim_ik_s2.flatten(),bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 2, Scenario 60% Sand Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(144)\n",
    "plt.hist(sim_ik_s3.flatten(),bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 3, Scenario 50% Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1., wspace=0.2, hspace=0.2)\n",
    "plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There may be a combination of erogodic fluctuations and control from the data.  \n",
    "\n",
    "* if we have a high degree of spatial correlation and dense data the global proportions are constrained by the data.\n",
    "\n",
    "#### Sequential Indicator Simulation with Ordinary Kriging\n",
    "\n",
    "Now let's run a realization with ordinary kriging.\n",
    "\n",
    "* relax the assumption of stationary facies proportions, estimate the local facies proportion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73073\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_ikok = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=dummy_trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73073,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=1,vario=varios)\n",
    "\n",
    "xmin = 0.0; xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cmap = plt.cm.inferno\n",
    "\n",
    "plt.subplot(131)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ikok,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Ordinary Kriging','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=4.0, top=1.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Locally Variable Proportions / Cateogrical Trends\n",
    "\n",
    "Next let's include a trend model, also called a locally variable proportion model in the case of categorical simulation. \n",
    "\n",
    "* We will make up a simple linear trend in X for demonstration. Note with kriging option 2 and a trend model with a ndarray of dimensions [ny,nx,ncut] the program will load the local proportion to apply to 1 - sum of the weights.  Note: this is not the trend / residual workflow in this first version of the program.  \n",
    "\n",
    "We make no attempt to fit a trend to the data. \n",
    "\n",
    "* Let's just make up a trend model for demonstration. Here's our trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global proportion of shale = 0.398\n",
      "Global proportion of sand = 0.602\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a simple trend model\n",
    "trend = np.zeros((nx,ny,ncut)); trend[:,:,0] = 0.4; trend[:,:,1] = 0.6\n",
    "for iy in range(0,ny):\n",
    "    for ix in range(0,nx):\n",
    "        trend[iy,ix,0] = trend[iy,ix,0] + ((ix-50)/nx) * 0.4\n",
    "        trend[iy,ix,1] = trend[iy,ix,1] - ((ix-50)/nx) * 0.4\n",
    "        \n",
    "trend = geostats.correct_trend(trend)\n",
    "\n",
    "print('Global proportion of shale = ' + str(np.average(trend[:,:,0].flatten())) )\n",
    "print('Global proportion of sand = ' +  str(np.average(trend[:,:,1].flatten())) )\n",
    "\n",
    "plt.subplot(121)\n",
    "GSLIB.pixelplt_st(trend[:,:,0],xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Shale Trend','X(m)','Y(m)','Shale Probability',cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.pixelplt_st(trend[:,:,1],xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Sand Trend','X(m)','Y(m)','Sand Probability',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we run the simulation, let's go ahead and shorted the variogram range. This will allow us to see more influence from the trend (reduce the local data constraint)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73073\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varios = []                                # the variogram list\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=50,hmin1=50)) # shale indicator variogram\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=50,hmin1=50)) # sand indicator variogram\n",
    "\n",
    "sim_ik_trend = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=trend,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73073,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=2,vario=varios)\n",
    "\n",
    "xmin = 0.0; xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cmap = plt.cm.inferno\n",
    "\n",
    "plt.subplot(131)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik_trend,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Locally Variable Proportion','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=4.0, top=1.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trend is quite epic. We should take care to ensure that the global proportions in the trend honor the representative statistics (from declustering).  The above exercise was just a simple demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low Trend: Global proportion of shale = 0.498\n",
      "Low Trend: Global proportion of sand = 0.502\n",
      "High Trend: Global proportion of shale = 0.298\n",
      "High Trend: Global proportion of sand = 0.702\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using trend scenarios\n",
    "trend_low = np.zeros((ny,nx,2))\n",
    "trend_low[:,:,0] = trend[:,:,0] + 0.1\n",
    "trend_low[:,:,1] = trend[:,:,1] - 0.1\n",
    "trend_low = geostats.correct_trend(trend_low)\n",
    "\n",
    "trend_high = np.zeros((ny,nx,2))\n",
    "trend_high[:,:,0] = trend[:,:,0] - 0.1\n",
    "trend_high[:,:,1] = trend[:,:,1] + 0.1\n",
    "trend_high = geostats.correct_trend(trend_high)\n",
    "\n",
    "print('Low Trend: Global proportion of shale = ' + str(np.average(trend_low[:,:,0].flatten())) )\n",
    "print('Low Trend: Global proportion of sand = ' +  str(np.average(trend_low[:,:,1].flatten())) )\n",
    "\n",
    "print('High Trend: Global proportion of shale = ' + str(np.average(trend_high[:,:,0].flatten())) )\n",
    "print('High Trend: Global proportion of sand = ' +  str(np.average(trend_high[:,:,1].flatten())) )\n",
    "\n",
    "plt.subplot(221)\n",
    "GSLIB.pixelplt_st(trend_low[:,:,0],xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Shale Trend Low','X(m)','Y(m)','Shale Probability',cmap)\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.pixelplt_st(trend_low[:,:,1],xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Sand Trend Low','X(m)','Y(m)','Sand Probability',cmap)\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.pixelplt_st(trend_high[:,:,0],xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Shale Trend High','X(m)','Y(m)','Shale Probability',cmap)\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.pixelplt_st(trend_high[:,:,1],xmin,xmax,ymin,ymax,xsiz,0.0,1.0,'Sand Trend High','X(m)','Y(m)','Sand Probability',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again we use a shorter variogram range to observe the impact of the trend model on the realizations.\n",
    "\n",
    "* see how the trend also constrains the global proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73073\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n",
      "Data for IK3D: Variable column Facies\n",
      "  Number   = 50\n",
      "Setting up rotation matrices for variogram and search\n",
      "Working on a single realization, seed 73073\n",
      "   currently on node 0\n",
      "   currently on node 1000\n",
      "   currently on node 2000\n",
      "   currently on node 3000\n",
      "   currently on node 4000\n",
      "   currently on node 5000\n",
      "   currently on node 6000\n",
      "   currently on node 7000\n",
      "   currently on node 8000\n",
      "   currently on node 9000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varios = []                                # the variogram list\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=100,hmin1=100)) # shale indicator variogram\n",
    "varios.append(GSLIB.make_variogram(nug=0.0,nst=1,it1=1,cc1=1.0,azi1=0,hmaj1=100,hmin1=100)) # sand indicator variogram\n",
    "\n",
    "sim_ik_lowtrend = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=trend_low,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73073,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=2,vario=varios)\n",
    "\n",
    "sim_ik_hightrend = geostats.sisim(df,'X','Y','Facies',ivtype=0,koption=0,ncut=2,thresh=thresh,gcdf=gcdf,trend=trend_high,\n",
    "               tmin=tmin,tmax=tmax,zmin=0.0,zmax=1.0,ltail=1,ltpar=1,middle=1,mpar=0,utail=1,utpar=2,\n",
    "               nx=nx,xmn=xmn,xsiz=xsiz,ny=ny,ymn=ymn,ysiz=ysiz,seed = 73073,\n",
    "               ndmin=ndmin,ndmax=ndmax,nodmax=nodmax,mults=1,nmult=3,noct=-1,radius=radius,ktype=2,vario=varios)\n",
    "\n",
    "xmin = 0.0; xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cmap = plt.cm.inferno\n",
    "\n",
    "plt.subplot(121)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik_lowtrend,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Locally Variable Proportion - Low Trend','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplot(122)                                          # plot the results\n",
    "GSLIB.locpix_st(sim_ik_hightrend,xmin,xmax,ymin,ymax,xsiz,-.4,1.0,df,'X','Y','Facies','Sequential Indicator Simulation - Locally Variable Proportion - High Trend','X(m)','Y(m)','Facies',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=4.0, top=1.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the global proportions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(141)\n",
    "plt.hist(df['Facies'].values,bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Well Data Facies Proportions')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(142)\n",
    "plt.hist(sim_ik_trend.flatten(),bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 1, Base Case Trend')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(143)\n",
    "plt.hist(sim_ik_lowtrend.flatten(),bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 1, Low Trend Scenario')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplot(144)\n",
    "plt.hist(sim_ik_hightrend.flatten(),bins=2,density=True,cumulative = True,alpha=0.1,color='red',edgecolor='black')\n",
    "plt.xlabel('Facies (0-shale, 1-sand)')\n",
    "plt.title('Realization 1, High Trend Scenario')\n",
    "plt.ylabel('Cumulation Frequency')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1., wspace=0.2, hspace=0.2)\n",
    "plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The base case honour the well data proportions. While the low case boosts the shale to 50% (likely fell short due to data constraints) and the high case discounts the shale to 30%. \n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of spatial simulation with seuquential indicator simulation. I know the simulation program is rough and the code needs some clean up, but we are getting to the point where we have a function package for Geostatistics in Python.  \n",
    "\n",
    "Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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